
==== Front
BMC Nephrol
BMC Nephrol
BMC Nephrology
1471-2369
BioMed Central London

39237885
3736
10.1186/s12882-024-03736-2
Research
Asymmetric dimethylarginine serum concentration in normal weight and obese CKD patients treated with hemodialysis
Alipoor Elham 12
Salehi Shiva 2
Dehghani Sahar 2
Yaseri Mehdi 3
http://orcid.org/0000-0002-5787-4089
Hosseinzadeh-Attar Mohammad Javad hosseinzadeh.md.phd@gmail.com
mhosseinzadeh@tums.ac.ir

12
1 https://ror.org/01c4pz451 grid.411705.6 0000 0001 0166 0922 Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
2 https://ror.org/01c4pz451 grid.411705.6 0000 0001 0166 0922 Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, No#44, Hojjatdoost St., Naderi St., Keshavarz Blvd, Tehran, Iran
3 https://ror.org/01c4pz451 grid.411705.6 0000 0001 0166 0922 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
5 9 2024
5 9 2024
2024
25 2947 1 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Introduction

Asymmetric dimethylarginine (ADMA), a cardiovascular risk factor, increases in renal failure. The aim of this study was to investigate ADMA levels in normal weight and obese patients on hemodialysis.

Methods

In this cross-sectional study, 43 normal weight and 43 obese patients on regular hemodialysis were examined. Malnutrition-inflammation score (MIS), anthropometry, circulating ADMA, lipid profiles including triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL) and lipid ratios, glucose homeostasis parameters, blood pressure, and high-sensitivity C-reactive protein (hs-CRP) were assessed.

Results

Serum levels of ADMA were significantly lower in the obese compared to the normal weight patients (10268.2 ± 10092.4 vs. 13765.2 ± 9951.3 ng/l, P = 0.03). At the same time MIS score (6.1 ± 2.4 vs. 10.7 ± 3.2, P < 0.001), systolic blood pressure (119 ± 26.8 vs. 134.2 ± 24.7 mmHg, P = 0.018) and mean arterial pressure (91.3 ± 18.6 vs. 100.9 ± 15.9 mmHg, P = 0.028) were significantly lower in the obese than the normal weight group. Fasting blood glucose (P = 0.045), TG/HDL (P = 0.03), TC/HDL (P = 0.019), and LDL/HDL (P = 0.005) ratios, and hs-CRP (P = 0.015) levels were significantly higher in the obese than in the normal weight group.

Conclusion

Circulating ADMA was significantly lower in obese than in normal weight patients on hemodialysis, which was concomitant with lower MIS, indicating a better nutritional inflammatory status, and lower blood pressure.

Keywords

Asymmetric dimethylarginine
Hemodialysis
Malnutrition inflammation score
Obesity
http://dx.doi.org/10.13039/501100004484 Tehran University of Medical Sciences and Health Services 37227 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Chronic kidney disease (CKD) is associated with an increased risk of atherosclerosis and cardiovascular mortality. In end-stage renal disease (ESRD), cardiac events account for more than half of all deaths [1]. Endothelial dysfunction as a result of reduced nitric oxide (NO) bioavailability is a key factor in the progression of atherosclerosis. Asymmetric dimethylarginine (ADMA), a competitive inhibitor of endothelial NO synthase (eNOS), is associated with endothelial dysfunction, inflammation, and oxidative stress in cardiovascular diseases and renal failure [2, 3]. ADMA has also been implicated in lipid metabolism and hypercholesterolemia, hypertension, insulin resistance and diabetes.

ADMA accumulation has been described in patients with different degrees of renal failure [4, 5]. Some reasons for the accumulation of ADMA in CKD patients include increased protein methylation and turnover, decreased ADMA metabolism by dimethylarginine dimethylaminohydrolase (DDAH), and impaired renal function [6]. ADMA levels increase in the early stages of CKD, even before GFR declines. ADMA has a relatively low molecular weight. In ESRD, contrary to expectations, some studies have failed to show significant elimination of ADMA after dialysis, even with high-flux membranes or hemodiafiltration. Changes in DDAH activity, inflammatory cytokines and PH may be involved in these controversies [7].

ADMA is also known as an adipokine, because the genes encoding its biosynthesis and metabolism are also located in adipose tissue as well [8]. Adipokines may be associated with inflammation, cardiovascular risk factors, and protein energy wasting (PEW) in ESRD [9, 10]; however, few studies have investigated these peptides considering the role of body mass status. ADMA levels are elevated with increasing body mass index (BMI) and insulin resistance in individuals with normal renal function [11]. However, studies investigating changes in ADMA levels in relation to body weight status in CKD patients have yielded conflicting results. While, one study found a positive correlation between ADMA and BMI in kidney transplant recipients, which was attributed to hypercholesterolemia and oxidative stress-induced disruption of DDAH activity [12], some others showed no significant relationship between ADMA concentration and BMI and overweight in CKD [13, 14]. On the other hand, one study showed an association of high ADMA levels with low BMI and albumin levels in hemodialysis patients, indicating poor nutritional status and abnormal protein metabolism [15].

Given the recognition of ADMA as a cardiovascular risk factor, its elevation in renal failure, and the controversies about its changes with BMI status in hemodialysis, the aim of this study was to investigate ADMA concentrations in normal weight and obese patients on hemodialysis in relation to nutritional status and cardiometabolic risk factors.

Materials and methods

Design and patients

In this comparative cross-sectional study, 43 normal weight (18.5 ≤ BMI < 25 kg/m2) and 43 obese (BMI ≥ 30 kg/m2) patients with ESRD (glomerular filtration rate < 15 mL/min) who had been on regular hemodialysis for more than six months were evaluated. Patients with a history of any endocrinopathies other than diabetes, inflammatory and immune disorders, organ failure, malignancy, or recent acute illness (such as trauma, infection, myocardial infarction, or cerebrovascular accident) were not eligible for this study. Additionally, patients treated with glucocorticoids, taking high doses of dietary supplements, with recent intentional weight changes, smokers and drug addicts were not included in this study. Patients were recruited from hemodialysis units affiliated with medical universities. All participants have completed and signed a written informed consent form.

Assessments

A 10-hour fasting venous blood sample was collected prior to dialysis, and serum was stored at -80 °C after centrifugation. Anthropometric indices were assessed using standard techniques immediately after completion of hemodialysis to minimize the effect of fluid overload.

The malnutrition inflammation score (MIS) consists of 10 questions, 7 of which are subjective global assessment (SGA) items, in addition to BMI level, and blood levels of albumin and total iron binding capacity (TIBC). Each item is scored from 0 (normal) to 3 (severe), and the total score ranges from 0 to 30, with higher scores indicating more severe PEW [16]. Mean dietary intakes of energy and protein were extracted from dietary recalls of two dialysis and two non-dialysis days, using Nutritionist IV software (N Squared Computing, San Bruno, CA, USA).

Serum ADMA levels were determined using an enzyme-linked immunosorbent assay (ELISA) kit (Crystal Day Biotech Co., China). Serum levels of fasting blood glucose (FBG), albumin, transferrin, lipid profiles including total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and high-sensitivity C-reactive protein (hs-CRP), and creatinine were measured by standard biochemical methods using commercial kits (Pars-Azmoon, Tehran, Iran). TIBC was calculated as transferrin×1.25. Serum insulin levels were measured using a radioimmunoassay kit (DiaSource, Louvain-la-Neuve, Belgium), and homeostatic model assessment for insulin resistance (HOMA-IR) was calculated based on relevant formulas.

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in the sitting position after 10 min of rest. Mean arterial pressure (MAP) was calculated as (SBP + 2 DBP)/3.

Fresenius 4008s dialysis machine, PS13 low-flux (Meditechsys Co, Tehran, Iran) dialyzer membrane, and 200–300 ml/min blood flow were used as the standard dialysis unit protocol in this study. The Kt/V was calculated as “-ln{[(post BUN/pre BUN)- (0.008×t)]+[4-(3.5×post BUN/pre BUN)] × 0.55×UF/V}” [17].

Statistical analysis

The mean, standard deviation (SD), frequencies, and percentages were used to present the results of this research. The Kolmogorov-Smirnov test was used to test the normality of the distribution of the variables. To compare differences in variables between the two study groups, the Chi-Squared test was used for nominal variables, and the Independent Sample t-test and Mann–Whitney test were used for continuous variables, as appropriate. Bivariate correlations were used to assess the relationship between ADMA and other variables. A general linear model was used to assess the effect of potential confounders, including age, sex, and diabetes. To find the most important factors associated with ADMA levels, a general linear model with LR backward model selection method was performed on variables that showed a p-value < 0.2 in univariate analyses, including BMI, waist circumference, TC, LDL, MIS, diabetes, dietary intake of protein (%), and energy, in addition to age, sex, and diabetes. The final model was reported by adjusted regression coefficient (Aβ), corresponding 95% confidence interval (CI), and P values. All statistical analyses were performed with SPSS (IBM SPSS Statistics for Windows, IBM Corp., Version 22.0. Armonk, NY, USA). P values less than 0.05 were considered statistically significant.

Results

General characteristics and nutritional status

There were no significant differences in sex (P = 0.084), age (P = 0.314), hemodialysis duration (P = 0.769), or dialysis adequacy (P = 0.766) between the obese and normal weight groups. Approximately 54% of the obese group and 31% of the normal weight group had diabetes (P = 0.048). Statistically significant differences in dry weight, BMI, and waist circumference were observed between the two groups (P < 0.001) (Table 1).

Table 1 General characteristics, anthropometric indices, and nutritional status of the study population

	Normal weight (n = 43)	Obese (n = 43)	P	
Sex	Male	25 (58.1)	16 (37.2)	0.084 *	
	Female	18 (41.9)	27 (62.8)		
Age (yr)	57.7 ± 14.5	57.1 ± 10.7	0.314 **	
Duration of dialysis (month)	47.9 ± 36.6	46.8 ± 38.5	0.769 **	
Kt/V	1.3 ± 0.6	1.3 ± 0.5	0.766 **	
Diabetes	13 (31.0)	23 (53.5)	0.048 *	
Weight (kg)	55.3 ± 7.2	83.1 ± 10.5	< 0.001 **	
BMI (kg/m2)	21.8 ± 2	33.4 ± 2.9	< 0.001 **	
Waist circumference (cm)	83.5 ± 8.5	111.4 ± 8.5	< 0.001 **	
MIS	10.7 ± 3.2	6.1 ± 2.4	< 0.001 **	
Albumin (mg/dl)	4.1 ± 0.4	4.1 ± 0.3	0.869 **	
TIBC (mg/dl)	188.5 ± 50.8	209.2 ± 60.6	0.09 ***	
Energy (kcal/kg)	33.2 ± 12.7	32.2 ± 13.9	0.681 **	
Protein (g/kg)	1.4 ± 0.5	1.3 ± 0.6	0.167 ***	
Values are presented as mean ± SD or frequency (%)

* Based on Chi-Square test

** Based on Mann–Whitney test

*** Based on t-test

The MIS score was significantly lower in the obese patients than in the normal weight patients (P < 0.001). No other differences in nutritional status parameters such as albumin and TIBC or dietary intakes of energy and protein were observed between the two study groups.

ADMA

The present study showed significantly lower circulating levels of ADMA in the obese patients compared to the normal weight patients (10268.2 ± 10092.4 vs. 13765.2 ± 9951.3 ng/l, P = 0.028). This significant difference remained unchanged after adjustment for the effects of age, sex, and diabetes (P = 0.03) (Table 2). Additionally, when patients were divided into two groups based on median values of waist circumference, ADMA levels were slightly lower in patients with higher abdominal obesity (10369.3 ± 10588.7 vs. 13494 ± 9908.1 ng/l, P = 0.05). There were no significant differences in ADMA levels when patients were categorized based on hs-CRP or HOMA-IR levels (data not shown).

Table 2 Circulating ADMA, biochemical parameters and blood pressure of the study population

	Normal weight (n = 43)	Obese (n = 43)	P 1	P 2	P 3	
ADMA (ng/l)*	13765.2 ± 9951.3	10268.2 ± 10092.4	0.028 **	0.027	0.03	
TG (mg/dl)	146.3 ± 138.4	196.4 ± 144.1	0.002 ***	0.136	0.269	
TC (mg/dl)	139.8 ± 40.9	153.8 ± 48.9	0.154 **	0.188	0.143	
HDL (mg/dl)	43.2 ± 10.5	38.4 ± 15	0.092 **	0.041	0.058	
LDL (mg/dl)	72.4 ± 26.4	81.3 ± 28.8	0.142 **	0.161	0.07	
TG/HDL	3.5 ± 3.6	5.7 ± 5.4	0.001 ***	0.024	0.03	
TC/HDL	3.5 ± 1.9	4.5 ± 2.4	0.007 ***	0.023	0.019	
LDL/HDL	1.8 ± 0.9	2.4 ± 1.2	0.007 ***	0.01	0.005	
FBG (mg/dl)	104.6 ± 32	140 ± 67.4	0.013 ***	0.003	0.045	
Fasting insulin (µIU/mL)	39.4 ± 45.2	38.3 ± 33.3	0.796 ***	0.898	0.51	
HOMA-IR	10.1 ± 13.4	13.1 ± 13	0.26 ***	0.317	0.984	
Creatinine (mg/dl)	8.9 ± 2.3	9.6 ± 2.7	0.268 **	0.061	0.023	
hs-CRP (mg/dl)	6.9 ± 6.9	9.4 ± 8	0.317 ***	0.047	0.015	
SBP (mmHg)	134.2 ± 24.7	119 ± 26.8	0.002 ***	0.026	0.018	
DBP (mmHg)	84.2 ± 12.3	77.4 ± 15	0.011 ***	0.055	0.054	
MAP (mmHg)	100.9 ± 15.9	91.3 ± 18.6	0.004 ***	0.034	0.028	
* Mean ± SD values were reported based on the main ADMA values. P value was obtained based on t-test using log 10 transformed values

** Based on t-test

*** Based on Mann–Whitney test

P1: P in the crude model

P2: P adjusted for age and sex based on General Linear Model

P3: P adjusted for age, sex and diabetes based on General Linear Model

There were significant inverse associations between ADMA and dry body weight (r=-0.236, P = 0.03) and BMI (r=-0.266, P = 0.014) in this study. No other significant relationship was observed between ADMA concentration and other cardiometabolic risk factors (P > 0.05, data not shown).

The final logistic regression model from the backward selection model showed that age and BMI were the most important determinants of ADMA concentration. Each unit increase in age (Aβ=-0.237, 95% CI: -0.014 to 0, P = 0.046) and BMI (Aβ=-0.253, 95% CI: -0.031 to -0.001, P = 0.033) was associated with approximately about 0.24 and 0.25 unit decrease in ADMA levels, respectively.

Cardiovascular risk factors

The results showed significantly higher FBG (P = 0.045), TG/HDL ratio (P = 0.030), TC/HDL ratio (P = 0.019), LDL/HDL ratio (P = 0.005), and hs-CRP levels (P = 0.015) in the obese group than in the normal weight group after adjustment for age, sex, and diabetes (Table 2). In the current study, significantly lower SBP (P = 0.018) and MAP (P = 0.028) and a marginally lower DBP (P = 0.054) values were observed in the obese group compared with the normal weight group after adjustment for potential covariates. No differences in other lipid profiles, creatinine and insulin resistance were observed between the two study groups.

Discussion

The results of this study showed a significantly lower ADMA concentration in the obese compared to the normal weight patients on hemodialysis. This finding was associated with lower MIS, presenting better nutritional and inflammatory status, and lower blood pressure in the obese group. Age and BMI were the most important determinants of ADMA concentration in this study.

In contrast to the current findings, a study found a positive relationship between ADMA concentration and BMI, dry body weight, and fat mass in CKD patients [12]. It has been suggested that obesity-associated hypercholesterolemia and oxidative stress disrupt DDAH activity, leading to an increase in ADMA levels [12]. However, similar to our findings, another study showed an inverse relationship between BMI and serum albumin with ADMA levels in hemodialysis patients. It has been suggested that poor nutritional status and abnormal protein metabolism correlate with higher ADMA concentration [15]. The current results also showed lower MIS levels in the obese group than in the normal weight group, which is a valid indicator of nutritional and inflammatory status in CKD. In addition, in patients on peritoneal dialysis, those with PEW based on subjective global assessment and other scores, have been shown to have higher ADMA levels than the group with good nutritional status. Patients with PEW and higher ADMA levels also had lower mean BMI, body fat percentage, and lean body mass, which were not significantly different from the non-PEW group [18]. Additionally, ADMA has been suggested as a potential biomarker for cancer cachexia, as skeletal muscle wasting may be a source of circulating ADMA and it may also contribute to impaired muscle protein synthesis as well [19].

Uremic malnutrition is associated with a variety of disorders, including hemodialysis-related catabolism, hormonal and metabolic changes, and chronic inflammation [20]. Inflammatory cytokines such as interleukin-6 and tumor necrosis factor alpha suppress appetite and promote muscle wasting and hypoalbuminemia [21]. Loss of muscle and adipose tissue and inflammation ultimately lead to an increased risk of cardiovascular disease and death [22]. Thus, the malnutrition-inflammation-cachexia syndrome appears to be involved in the obesity paradox observed in ESRD and other chronic diseases [23]. The obesity paradox refers to lower morality in overweight and obese patients with ESRD and some other diseases. Obesity may reduce the risk of malnutrition and PEW [24]. Greater body mass provides protection against significant protein loss in the inflammatory response. Other potential mechanisms include the priority of short-term survival benefits of obesity over its long-term deleterious effects, better short-term hemodynamic stability, altered cytokine profiles, and more effective sequestration of uremic toxins in obese patients [25]. Based on models of normal renal function, a direct relationship between ADMA concentration and increasing body fat mass was expected. Previous data suggest that although obesity may be protective in ESRD, this effect is limited to those with normal or high muscle mass. In fact, patients with high BMI due to high body fat showed no survival benefit [26]. Thus, the obese patients in this study, who also had lower MIS and signs of wasting, may have a higher proportion of muscle mass, which partly justifies the lack of higher ADMA levels in this group. It seems that the lower ADMA concentration, a well-recognized cardiovascular risk factor, in the obese patients in this study is consistent with the better outcomes that have been extensively proposed in this BMI group in other studies [27].

There are some reports on the effects of aspirin, statins, antihypertensive and hypoglycemic medications on ADMA levels; however, the exact effects of each drug are not yet known [28]. In general, NOS substrates and medications that decline ADMA concentration are expected to reduce blood pressure. The normal weight group in this study had higher ADMA concentrations despite higher intake of antihypertensive medications. The increasing or decreasing effects of different classes of antihypertensive medications, such as angiotensin-converting enzyme inhibitors and angiotensin receptor blockers [29–31], and even drugs within a subclass, such as captopril vs. enalapril [29, 32], on ADMA levels are controversial. Additionally, the obese patients had lower blood pressure. It is not clear that whether ADMA levels are directly affected by antihypertensive medications or are influenced by changes in blood pressure [7]. Thus, overall, these medications do not seem to be a major determinant of ADMA levels in this study. Future studies should clarify the exact effect of different drugs on ADMA concentration.

Patients with obesity had lower SBP, MAP, and slightly lower DBP with concomitant lower ADMA concentration compared to the normal weight group. The relationship between ADMA and blood pressure is well known because ADMA reduces the bioavailability of NO [33], which could result in impaired vasodilation, vascular stiffness, and organ perfusion, as well as impaired antithrombotic and anti-inflammatory effects [34]. Some studies have also reported a direct correlation between ADMA and CRP/hs-CRP concentrations in advanced renal disease [13, 35]. However, the effect of BMI or nutritional status was not considered. In contrast to these results, in the current study, the mean hs-CRP level was higher in the obese group compared with the normal weight group, which was in the opposite direction with lower MIS and ADMA levels in the obese patients. Another study showed that peritoneal dialysis patients with PEW and high ADMA levels also had increased hs-CRP concentrations compared to the non-PEW group. However, no direct correlation between ADMA and hs-CRP was observed in these patients [18].

The association of ADMA with dyslipidemia and other metabolic risk factors is controversial. Some studies have failed to show an association between ADMA levels and cardiometabolic risk factors including LDL [36], while others suggest a direct association with TC and LDL levels [37]. It has also been shown that high levels of ADMA are associated with elevated TG rather than LDL [38]. It has been suggested that native or oxidized forms of LDL may upregulate the expression and activity of protein arginine methyltransferases, which increase ADMA levels [39]. Despite studies reporting an association between ADMA and LDL levels, statin therapy had no effect on circulating ADMA [40]. A significant strong and direct relationship between IR and ADMA levels has been described in normal renal function [39]. It was also found that in obese patients, ADMA levels were higher in those with IR compared to non-IR group [11]. Some mechanisms relate to the lack of access to NO in conditions associated with IR [41]. Thus, part of the unexpected results in ADMA levels in the obese group may be due to the lack of significant differences in IR compared to the normal weight patients.

Changes in the activity of DDAH under various conditions can lead to changes in ADMA levels. ADMA is catabolized by DDAH to dimethylamine and citrulline, and the DDAH activity is controlled by complex regulatory mechanisms [42]. It has been shown that mice overexpressing DDAH gained more weight after a high fat diet compared to eNOS-deficient mice. However, the increased NO bioavailability associated with higher DDAH levels led to the downregulation of markers of differentiated adipocytes. Additionally, some genes involved in protection against oxidative stress were upregulated in white adipose tissue of mice with DDAH overexpression [43], which may reduce the nitrosative stress caused by elevated levels of the free radical NO [44]. High DDHA levels may be responsible, at least in part, for the lower ADMA levels in the obese group.

The liver removes significant amounts of ADMA from the systemic circulation [45]. Malnutrition could affect various aspects of liver metabolic function through peroxisomal and mitochondrial dysfunction [46]. Thus, liver function, which was not assessed in the current study, may also play a role in determining ADMA levels in the study groups. Additionally, it has been reported that the synthesis and concentration of uremic toxins, such as ADMA, may be lower in larger patients because the main visceral organ that produce them (intestine and liver) have a lower mass relative to body weight and the distribution volume is greater in high BMI. The higher ratio of fat and muscle mass to visceral mass would be associated with lower levels of uremic toxins and the better outcomes as they are taken up and metabolized by these tissues [47, 48].

This study has some limitations, including the relatively small sample size and the lack of data on DDAH and NO levels. In addition, due to the cross-sectional design of the study, the proposed mechanisms to justify the results should be considered as hypotheses. Having control groups of normal weight and obese individuals with normal renal function would help to better understand the distinctive changes of ADMA in health and ESRD with respect to body fat mass. Additionally, performing bioimpedance measurements could better reflect the precise relationship of body composition and dietary differences with ADMA levels and other cardiometabolic markers. This study included both diabetic and nondiabetic patients that may affect the results.

Conclusion

The results of the current study showed that serum concentrations of ADMA were significantly lower in obese compared to normal weight hemodialysis patients, which was associated with lower MIS, indicating a better nutritional-inflammatory status, and lower blood pressure. There were no significant relationships between serum ADMA levels and traditional cardiometabolic risk factors, except for an inverse relationship with dry body weight and BMI. Lower ADMA levels in the obese patients, as a known cardiovascular risk factor, are consistent with the previously proposed obesity paradox in obese patients on dialysis.

Acknowledgements

Not applicable.

Author contributions

Conceptualization & design: EA, MY, MJHA; Data gathering: EA, SS, SD; Data analysis: EA, SS, MY; Writing – drafting: EA, SS, SD; Writing – review & editing: MY, MJHA; Supervision: MJHA. All authors reviewed and approved the final manuscript.

Funding

This study was supported by Tehran University of Medical Sciences & Health Services grant 37227.

Data availability

The data relating to this research are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Declarations

Ethics approval and consent to participate

The ethics committee of the Tehran University of Medical Sciences approved this study with ethics number IR.TUMS.VCR.REC.1396.4764. All participants have completed and signed an informed written consent. The study has been performed in accordance with the ethical standards of 1964 Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

ADMA Asymmetric dimethylarginine

BMI Body mass index

eNOS Endothelial NO synthase

ESRD End-stage renal disease

FBG Fasting blood glucose

CKD Chronic kidney disease

CI Confidence interval

DBP Diastolic blood pressure

DDAH Dimethylarginine dimethylaminohydrolase

HDL High-density lipoprotein

HOMA-IR Homeostatic model assessment for insulin resistance

Hs-CRP High sensitivity C-reactive protein

LDL Low-density lipoprotein

MAP Mean arterial pressure

MIS Malnutrition inflammation score

NO Nitric oxide

PEW Protein energy wasting

SBP Systolic blood pressure

SGA Subjective global assessment

SD Standard deviation

TG Triglycerides

TC Total cholesterol

TIBC Total iron-binding capacity

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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